Much attention and research has been paid toward medical applications using content-based image retrieval techniques. Some work has been done toward summarization of echocardiogram videos using visual contents such as color, shape, and the tracing of the Electrocardiogram (ECG) signal. However, limited efforts have been spent on diagnosis support of cardiomyopathies using echocardiography, advanced statistical classification and learning techniques.
Conventional computer-aided diagnosis (CAD) systems treat different inputs independently, such as between components of a numerical feature vector, between vectors of different modalities, and between numerical and symbolic inputs. Furthermore CAD systems make decisions in a sequential, rule-based, tree-like fashion. The disadvantage to this approach is that when the numerical feature inputs are unreliable, which is usually the case when using automated feature extraction instead of manual extraction; the system performance can degrade dramatically, depending upon the order in which the sequential decisions are arranged.
Another drawback of the traditional decision tree approaches is that each decision can only be made along existing feature dimensions, which is in turn limited by the prior selection of the feature components, without linear or nonlinear transformation invariance. Some recent general approaches using classification trees extends the traditional paradigm for decision tree construction and can use an aggregation of multiple trees to achieve higher capabilities.
There is a need for a CAD system capable of implementing probabilistic classification, content-based similarity comparisons and machine learning algorithms using multidimensional medical image databases in order to assist a medical professional to reach a medical diagnosis.